Maintenance of Data Cubes and Summary Tables in a Warehouse.

@inproceedings{DBLP:conf/sigmod/MumickQM97,
  author    = {Inderpal Singh Mumick and
               Dallan Quass and
               Barinderpal Singh Mumick},
  editor    = {Joan Peckham},
  title     = {Maintenance of Data Cubes and Summary Tables in a Warehouse},
  booktitle = {SIGMOD 1997, Proceedings ACM SIGMOD International Conference
               on Management of Data, May 13-15, 1997, Tucson, Arizona, USA},
  publisher = {ACM Press},
  year      = {1997},
  pages     = {100-111},
  ee        = {http://doi.acm.org/10.1145/253260.253277, db/conf/sigmod/MumickQM97.html},
  crossref  = {DBLP:conf/sigmod/97},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Data warehouses contain large amounts of information, often collected from a variety of independent sources. Decision-support functions in a warehouse, such as on-line analytical processing (OLAP), involve hundreds of complex aggregate queries over large volumes of data. It is not feasible to compute these queries by scanning the data sets each time. Warehouse applications therefore build a large number of summary tables, or materialized aggregate views, to help them increase the system performance.

As changes, most notably new transactional data, are collected at the data sources, all summary tables at the warehouse that depend upon this data need to be updated. Usually, source changes are loaded into the warehouse at regular intervals, usually once a day, in a batch window, and the warehouse is made unavailable for querying while it is updated. Since the number of summary tables that need to be maintained is often large, a critical issue for data warehousing is how to maintain the summary tables efficiently.

In this paper we propose a method of maintaining aggregate views (the summary-delta table method), and use it to solve two problems in maintaining summary tables in a warehouse: (1) how to efficiently maintain a summary table while minimizing the batch window needed for maintenance, and (2) how to maintain a large set of summary tables defined over the same base tables.

While several papers have addressed the issues relating to choosing and materializing a set of summary tables, this is the first paper to address maintaining summary tables efficiently.

Copyright © 1997 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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Referenced by

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19. Michael O. Akinde, Ole Guttorm Jensen, Michael H. Böhlen: Minimizing Detail Data in Data Warehouses. EDBT 1998: 293-307